Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age

NCT05433519 | Completed

• 1 other identifier: 21-3115
• Study Type: observational
• Target: 400
• Locations: 2 countries
• Sites: 2

Brief Summary

This is a prospective cohort study of women enrolled early in pregnancy, with randomization to determine the timing of three follow-up visits in the second and third trimester. At each of these follow-up visits, investigators will assess gestational age with the FAMLI technology and compare that estimate to the known gestational age established early in pregnancy.

Conditions

Gestational Age; Machine Learning; Pregnancy Related

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy of FAMLI Technology

  Difference in mean absolute error (MAE) of the index test and clinical reference standard in the primary evaluation window

  From 14 through 27 completed weeks of gestation

Secondary Outcomes (1)

• Mean absolute error in the secondary evaluation window

  From 28 through 36 completed weeks of gestation

Study Arms (1)

Pregnant Women

Pregnant women with gestational age established at less than 14 weeks of gestation

Eligibility Criteria

• Age: 18 Years - 59 Years
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Pregnant women at 14 weeks or earlier gestation

You may qualify if:

• years of age or older
• viable intrauterine pregnancy at less than 14 0/7 weeks of gestation
• ability and willingness to provide written informed consent
• intent to remain in current geographical area of residence for the duration of study
• willingness to adhere to study procedures

You may not qualify if:

• maternal body mass index = 40 kg/m\^2
• multiple gestation (i.e., twins or higher order)
• major fetal malformation or anomaly
• any other condition (social or medical) that, in the opinion of the study staff, would make study participation unsafe or complicate data interpretation.

Sponsors & Collaborators

• University of North Carolina, Chapel Hill: lead
• Bill and Melinda Gates Foundation: collaborator

Study Sites (2)

University of North Carolina

Chapel Hill, North Carolina, 27599, United States

Location

University Teaching Hospital

Lusaka, Zambia

Location

Related Publications (1)

• Stringer JSA, Pokaprakarn T, Prieto JC, Vwalika B, Chari SV, Sindano N, Freeman BL, Sikapande B, Davis NM, Sebastiao YV, Mandona NM, Stringer EM, Benabdelkader C, Mungole M, Kapilya FM, Almnini N, Diaz AN, Fecteau BA, Kosorok MR, Cole SR, Kasaro MP. Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps. JAMA. 2024 Aug 27;332(8):649-657. doi: 10.1001/jama.2024.10770.

  PMID: 39088200 DERIVED

Biospecimen

Retention: SAMPLES WITH DNA

Whole blood, plasma, serum, and urine

Study Officials

• Jeff Stringer, MD

  University of North Carolina

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 21, 2022
• First Posted: June 27, 2022
• Study Start: July 27, 2022
• Primary Completion: May 31, 2023
• Study Completion: November 13, 2023
• Last Updated: May 8, 2024

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will share
• Time Frame: 9-36 months following publication
• Access Criteria: noted above

Locations

US (1); ZA (1)